





"""simple docstring"""





import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.12.2"):
       raise Exception("requires fairseq >= 0.12.2")
if version.parse(fairseq.__version__) > version.parse("2"):
       raise Exception("requires fairseq < v2")

logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  "Hello, World!"
SCREAMING_SNAKE_CASE__   =  "en_XX"


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		bool     ):







      '''simple docstring'''




      lowerCAmelCase    		=			Path("""data_bin"""     )
      lowerCAmelCase    		=			FairseqXmodModel.from_pretrained(
          model_name_or_path=str(Path(SCREAMING_SNAKE_CASE     ).parent     )				,		checkpoint_file=Path(SCREAMING_SNAKE_CASE     ).name				,		_name="""xmod_base"""				,		arch="""xmod_base"""				,		task="""multilingual_masked_lm"""				,		data_name_or_path=str(SCREAMING_SNAKE_CASE     )				,		bpe="""sentencepiece"""				,		sentencepiece_model=str(Path(SCREAMING_SNAKE_CASE     ).parent / """sentencepiece.bpe.model"""     )				,		src_dict=str(data_dir / """dict.txt"""     )				,		)
      xmod.eval()  # disable dropout
      print(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			xmod.model.encoder.sentence_encoder
      lowerCAmelCase    		=			XmodConfig(
          vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings				,		hidden_size=xmod.cfg.model.encoder_embed_dim				,		num_hidden_layers=xmod.cfg.model.encoder_layers				,		num_attention_heads=xmod.cfg.model.encoder_attention_heads				,		intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim				,		max_position_embeddings=5_14				,		type_vocab_size=1				,		layer_norm_eps=1e-5				,		pre_norm=xmod.cfg.model.encoder_normalize_before				,		adapter_reduction_factor=getattr(xmod.cfg.model				,		"""bottleneck"""				,		2     )				,		adapter_layer_norm=xmod.cfg.model.adapter_layer_norm				,		adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm				,		ln_before_adapter=xmod.cfg.model.ln_before_adapter				,		languages=xmod.cfg.model.languages				,		)
      if classification_head:
            lowerCAmelCase    		=			xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

      print("""Our X-MOD config:"""				,		SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			XmodForSequenceClassification(SCREAMING_SNAKE_CASE     ) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE     )
      model.eval()

      # Now let's copy all the weights.
      # Embeddings
      lowerCAmelCase    		=			xmod_sent_encoder.embed_tokens.weight
      lowerCAmelCase    		=			xmod_sent_encoder.embed_positions.weight
      lowerCAmelCase    		=			torch.zeros_like(
          model.roberta.embeddings.token_type_embeddings.weight     )  # just zero them out b/c xmod doesn't use them.

      lowerCAmelCase    		=			xmod_sent_encoder.layernorm_embedding.weight
      lowerCAmelCase    		=			xmod_sent_encoder.layernorm_embedding.bias

      for i in range(config.num_hidden_layers     ):
            # Encoder: start of layer
            lowerCAmelCase    		=			model.roberta.encoder.layer[i]
            lowerCAmelCase    		=			xmod_sent_encoder.layers[i]

            # self attention
            lowerCAmelCase    		=			layer.attention.self
            if not (
                xmod_layer.self_attn.k_proj.weight.data.shape
                == xmod_layer.self_attn.q_proj.weight.data.shape
                == xmod_layer.self_attn.v_proj.weight.data.shape
                == torch.Size((config.hidden_size, config.hidden_size)     )
            ):
                  raise AssertionError("""Dimensions of self-attention weights do not match."""     )

            lowerCAmelCase    		=			xmod_layer.self_attn.q_proj.weight
            lowerCAmelCase    		=			xmod_layer.self_attn.q_proj.bias
            lowerCAmelCase    		=			xmod_layer.self_attn.k_proj.weight
            lowerCAmelCase    		=			xmod_layer.self_attn.k_proj.bias
            lowerCAmelCase    		=			xmod_layer.self_attn.v_proj.weight
            lowerCAmelCase    		=			xmod_layer.self_attn.v_proj.bias

            # self-attention output
            lowerCAmelCase    		=			layer.attention.output
            if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
                  raise AssertionError("""Dimensions of self-attention output weights do not match."""     )
            lowerCAmelCase    		=			xmod_layer.self_attn.out_proj.weight
            lowerCAmelCase    		=			xmod_layer.self_attn.out_proj.bias
            lowerCAmelCase    		=			xmod_layer.self_attn_layer_norm.weight
            lowerCAmelCase    		=			xmod_layer.self_attn_layer_norm.bias

            # intermediate
            lowerCAmelCase    		=			layer.intermediate
            if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
                  raise AssertionError("""Dimensions of intermediate weights do not match."""     )
            lowerCAmelCase    		=			xmod_layer.fca.weight
            lowerCAmelCase    		=			xmod_layer.fca.bias

            # output
            lowerCAmelCase    		=			layer.output
            if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
                  raise AssertionError("""Dimensions of feed-forward weights do not match."""     )
            lowerCAmelCase    		=			xmod_layer.fca.weight
            lowerCAmelCase    		=			xmod_layer.fca.bias
            lowerCAmelCase    		=			xmod_layer.final_layer_norm.weight
            lowerCAmelCase    		=			xmod_layer.final_layer_norm.bias
            if bert_output.adapter_layer_norm is not None:
                  lowerCAmelCase    		=			xmod_layer.adapter_layer_norm.weight
                  lowerCAmelCase    		=			xmod_layer.adapter_layer_norm.bias

            if sorted(bert_output.adapter_modules.keys()     ) != sorted(xmod_layer.adapter_modules.keys()     ):
                  raise AssertionError("""Lists of language adapters do not match."""     )
            for lang_code, adapter in xmod_layer.adapter_modules.items():
                  lowerCAmelCase    		=			bert_output.adapter_modules[lang_code]
                  lowerCAmelCase    		=			xmod_layer.adapter_modules[lang_code]
                  lowerCAmelCase    		=			from_adapter.fca.weight
                  lowerCAmelCase    		=			from_adapter.fca.bias
                  lowerCAmelCase    		=			from_adapter.fca.weight
                  lowerCAmelCase    		=			from_adapter.fca.bias

            # end of layer

      if xmod_sent_encoder.layer_norm is not None:
            lowerCAmelCase    		=			xmod_sent_encoder.layer_norm.weight
            lowerCAmelCase    		=			xmod_sent_encoder.layer_norm.bias

      if classification_head:
            lowerCAmelCase    		=			xmod.model.classification_heads["""mnli"""].dense.weight
            lowerCAmelCase    		=			xmod.model.classification_heads["""mnli"""].dense.bias
            lowerCAmelCase    		=			xmod.model.classification_heads["""mnli"""].out_proj.weight
            lowerCAmelCase    		=			xmod.model.classification_heads["""mnli"""].out_proj.bias
      else:
            # LM Head
            lowerCAmelCase    		=			xmod.model.encoder.lm_head.dense.weight
            lowerCAmelCase    		=			xmod.model.encoder.lm_head.dense.bias
            lowerCAmelCase    		=			xmod.model.encoder.lm_head.layer_norm.weight
            lowerCAmelCase    		=			xmod.model.encoder.lm_head.layer_norm.bias
            lowerCAmelCase    		=			xmod.model.encoder.lm_head.weight
            lowerCAmelCase    		=			xmod.model.encoder.lm_head.bias

      # Let's check that we get the same results.
      lowerCAmelCase    		=			xmod.encode(SCREAMING_SNAKE_CASE     ).unsqueeze(0     )  # batch of size 1
      model.roberta.set_default_language(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			model(SCREAMING_SNAKE_CASE     )[0]
      if classification_head:
            lowerCAmelCase    		=			xmod.model.classification_heads["""mnli"""](xmod.extract_features(SCREAMING_SNAKE_CASE     )     )
      else:
            lowerCAmelCase    		=			xmod.model(SCREAMING_SNAKE_CASE				,		lang_id=[SAMPLE_LANGUAGE]     )[0]
      print(our_output.shape				,		their_output.shape     )
      lowerCAmelCase    		=			torch.max(torch.abs(our_output - their_output     )     ).item()
      print(F'max_absolute_diff = {max_absolute_diff}'     )  # ~ 1e-7
      lowerCAmelCase    		=			torch.allclose(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		atol=1e-3     )
      print("""Do both models output the same tensors?"""				,		"""🔥""" if success else """💩"""     )
      if not success:
            raise Exception("""Something went wRoNg"""     )

      Path(SCREAMING_SNAKE_CASE     ).mkdir(parents=SCREAMING_SNAKE_CASE				,		exist_ok=SCREAMING_SNAKE_CASE     )
      print(F'Saving model to {pytorch_dump_folder_path}'     )
      model.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--xmod_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       parser.add_argument(
           "--classification_head", action="store_true", help="Whether to convert a final classification head."
       )
       SCREAMING_SNAKE_CASE__   =  parser.parse_args()
       convert_xmod_checkpoint_to_pytorch(
           args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )













"""simple docstring"""





import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__   =  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE__   =  {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

SCREAMING_SNAKE_CASE__   =  {
    "allenai/led-base-16384": 16_384,
}

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE							      = PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE							      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE							      = LEDTokenizer
      _SCREAMING_SNAKE_CASE							      = ['input_ids', 'attention_mask']





      def __init__(			self ,		lowercase=None ,		lowercase=None ,		lowercase=None ,		lowercase="replace" ,		lowercase="<s>" ,		lowercase="</s>" ,		lowercase="</s>" ,		lowercase="<s>" ,		lowercase="<unk>" ,		lowercase="<pad>" ,		lowercase="<mask>" ,		lowercase=False ,		lowercase=True ,		**lowercase ,		)						->	Any:
            super().__init__(
                lowercase ,		lowercase ,		tokenizer_file=lowercase ,		errors=lowercase ,		bos_token=lowercase ,		eos_token=lowercase ,		sep_token=lowercase ,		cls_token=lowercase ,		unk_token=lowercase ,		pad_token=lowercase ,		mask_token=lowercase ,		add_prefix_space=lowercase ,		trim_offsets=lowercase ,		**lowercase ,		)

            lowerCAmelCase    		=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
            if pre_tok_state.get("""add_prefix_space""" ,		lowercase					) != add_prefix_space:
                  lowerCAmelCase    		=			getattr(lowercase ,		pre_tok_state.pop("""type"""					)					)
                  lowerCAmelCase    		=			add_prefix_space
                  lowerCAmelCase    		=			pre_tok_class(**lowercase					)

            lowerCAmelCase    		=			add_prefix_space

            # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
            lowerCAmelCase    		=			"""post_processor"""
            lowerCAmelCase    		=			getattr(self.backend_tokenizer ,		lowercase ,		lowercase					)
            if tokenizer_component_instance:
                  lowerCAmelCase    		=			json.loads(tokenizer_component_instance.__getstate__()					)

                  # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                  if "sep" in state:
                        lowerCAmelCase    		=			tuple(state["""sep"""]					)
                  if "cls" in state:
                        lowerCAmelCase    		=			tuple(state["""cls"""]					)

                  lowerCAmelCase    		=			False

                  if state.get("""add_prefix_space""" ,		lowercase					) != add_prefix_space:
                        lowerCAmelCase    		=			add_prefix_space
                        lowerCAmelCase    		=			True

                  if state.get("""trim_offsets""" ,		lowercase					) != trim_offsets:
                        lowerCAmelCase    		=			trim_offsets
                        lowerCAmelCase    		=			True

                  if changes_to_apply:
                        lowerCAmelCase    		=			getattr(lowercase ,		state.pop("""type"""					)					)
                        lowerCAmelCase    		=			component_class(**lowercase					)
                        setattr(self.backend_tokenizer ,		lowercase ,		lowercase					)





      @property
      # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
      def        _snake_case     (			self					)						->	str:
            if self._mask_token is None:
                  if self.verbose:
                        logger.error("""Using mask_token, but it is not set yet."""					)
                  return None
            return str(self._mask_token					)





      @mask_token.setter
      def        _snake_case     (			self ,		lowercase					)						->	Optional[int]:
            lowerCAmelCase    		=			AddedToken(lowercase ,		lstrip=lowercase ,		rstrip=lowercase					) if isinstance(lowercase ,		lowercase					) else value
            lowerCAmelCase    		=			value





      def        _snake_case     (			self ,		*lowercase ,		**lowercase					)						->	BatchEncoding:
            lowerCAmelCase    		=			kwargs.get("""is_split_into_words""" ,		lowercase					)

            if is_split_into_words and not self.add_prefix_space:
                  raise ValueError(
                      f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                      """to use it with pretokenized inputs."""					)

            return super()._batch_encode_plus(*lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		*lowercase ,		**lowercase					)						->	BatchEncoding:
            lowerCAmelCase    		=			kwargs.get("""is_split_into_words""" ,		lowercase					)

            if is_split_into_words and not self.add_prefix_space:
                  raise ValueError(
                      f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                      """to use it with pretokenized inputs."""					)

            return super()._encode_plus(*lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase = None					)						->	Tuple[str]:
            lowerCAmelCase    		=			self._tokenizer.model.save(lowercase ,		name=lowercase					)
            return tuple(lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase=None					)						->	Any:
            lowerCAmelCase    		=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
            if token_ids_a is None:
                  return output

            return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





      def        _snake_case     (			self ,		lowercase ,		lowercase = None					)						->	List[int]:
            lowerCAmelCase    		=			[self.sep_token_id]
            lowerCAmelCase    		=			[self.cls_token_id]

            if token_ids_a is None:
                  return len(cls + token_ids_a + sep					) * [0]
            return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]





      def        _snake_case     (			self ,		lowercase ,		lowercase = None ,		lowercase = PaddingStrategy.DO_NOT_PAD ,		lowercase = None ,		lowercase = None ,		)						->	dict:
            lowerCAmelCase    		=			super()._pad(
                encoded_inputs=lowercase ,		max_length=lowercase ,		padding_strategy=lowercase ,		pad_to_multiple_of=lowercase ,		return_attention_mask=lowercase ,		)

            # Load from model defaults
            if return_attention_mask is None:
                  lowerCAmelCase    		=			"""attention_mask""" in self.model_input_names

            if return_attention_mask and "global_attention_mask" in encoded_inputs:
                  lowerCAmelCase    		=			encoded_inputs[self.model_input_names[0]]
                  # `global_attention_mask` need to have the same length as other (sequential) inputs.
                  lowerCAmelCase    		=			len(encoded_inputs["""global_attention_mask"""]					) != len(lowercase					)

                  if needs_to_be_padded:
                        lowerCAmelCase    		=			len(lowercase					) - len(encoded_inputs["""global_attention_mask"""]					)

                        if self.padding_side == "right":
                              # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                              lowerCAmelCase    		=			(
                                  encoded_inputs["""global_attention_mask"""] + [-1] * difference
                              )
                        elif self.padding_side == "left":
                              lowerCAmelCase    		=			[-1] * difference + encoded_inputs[
                                  """global_attention_mask"""
                              ]
                        else:
                              raise ValueError("""Invalid padding strategy:""" + str(self.padding_side					)					)

            return encoded_inputs






